Extracts statistically independent components from data. Only affects numerical features. See fastICA::fastICA for details.
Format
R6Class
object inheriting from PipeOpTaskPreproc
/PipeOp
.
Construction
id
::character(1)
Identifier of resulting object, default"ica"
.param_vals
:: namedlist
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Defaultlist()
.
Input and Output Channels
Input and output channels are inherited from PipeOpTaskPreproc
.
The output is the input Task
with all affected numeric parameters replaced by independent components.
State
The $state
is a named list
with the $state
elements inherited from PipeOpTaskPreproc
, as well as the elements of the function fastICA::fastICA()
,
with the exception of the $X
and $S
slots. These are in particular:
Parameters
The parameters are the parameters inherited from PipeOpTaskPreproc
, as well as the following parameters
based on fastICA()
:
n.comp
::numeric(1)
Number of components to extract. Default isNULL
, which sets it to the number of available numeric columns.alg.typ
::character(1)
Algorithm type. One of "parallel" (default) or "deflation".fun
::character(1)
One of "logcosh" (default) or "exp".alpha
::numeric(1)
In range[1, 2]
, Used for negentropy calculation whenfun
is "logcosh". Default is 1.0.method
::character(1)
Internal calculation method. "C" (default) or "R". SeefastICA()
.row.norm
::logical(1)
Logical value indicating whether rows should be standardized beforehand. Default isFALSE
.maxit
::numeric(1)
Maximum number of iterations. Default is 200.tol
::numeric(1)
Tolerance for convergence, default is1e-4
.verbose
logical(1)
Logical value indicating the level of output during the run of the algorithm. Default isFALSE
.w.init
::matrix
Initial un-mixing matrix. SeefastICA()
. Default isNULL
.
Internals
Uses the fastICA()
function.
Methods
Only methods inherited from PipeOpTaskPreproc
/PipeOp
.
See also
https://mlr3book.mlr-org.com/list-pipeops.html
Other PipeOps:
PipeOpEnsemble
,
PipeOpImpute
,
PipeOpTargetTrafo
,
PipeOpTaskPreprocSimple
,
PipeOpTaskPreproc
,
PipeOp
,
mlr_pipeops_boxcox
,
mlr_pipeops_branch
,
mlr_pipeops_chunk
,
mlr_pipeops_classbalancing
,
mlr_pipeops_classifavg
,
mlr_pipeops_classweights
,
mlr_pipeops_colapply
,
mlr_pipeops_collapsefactors
,
mlr_pipeops_colroles
,
mlr_pipeops_copy
,
mlr_pipeops_datefeatures
,
mlr_pipeops_encodeimpact
,
mlr_pipeops_encodelmer
,
mlr_pipeops_encode
,
mlr_pipeops_featureunion
,
mlr_pipeops_filter
,
mlr_pipeops_fixfactors
,
mlr_pipeops_histbin
,
mlr_pipeops_imputeconstant
,
mlr_pipeops_imputehist
,
mlr_pipeops_imputelearner
,
mlr_pipeops_imputemean
,
mlr_pipeops_imputemedian
,
mlr_pipeops_imputemode
,
mlr_pipeops_imputeoor
,
mlr_pipeops_imputesample
,
mlr_pipeops_kernelpca
,
mlr_pipeops_learner
,
mlr_pipeops_missind
,
mlr_pipeops_modelmatrix
,
mlr_pipeops_multiplicityexply
,
mlr_pipeops_multiplicityimply
,
mlr_pipeops_mutate
,
mlr_pipeops_nmf
,
mlr_pipeops_nop
,
mlr_pipeops_ovrsplit
,
mlr_pipeops_ovrunite
,
mlr_pipeops_pca
,
mlr_pipeops_proxy
,
mlr_pipeops_quantilebin
,
mlr_pipeops_randomprojection
,
mlr_pipeops_randomresponse
,
mlr_pipeops_regravg
,
mlr_pipeops_removeconstants
,
mlr_pipeops_renamecolumns
,
mlr_pipeops_replicate
,
mlr_pipeops_scalemaxabs
,
mlr_pipeops_scalerange
,
mlr_pipeops_scale
,
mlr_pipeops_select
,
mlr_pipeops_smote
,
mlr_pipeops_spatialsign
,
mlr_pipeops_subsample
,
mlr_pipeops_targetinvert
,
mlr_pipeops_targetmutate
,
mlr_pipeops_targettrafoscalerange
,
mlr_pipeops_textvectorizer
,
mlr_pipeops_threshold
,
mlr_pipeops_tunethreshold
,
mlr_pipeops_unbranch
,
mlr_pipeops_updatetarget
,
mlr_pipeops_vtreat
,
mlr_pipeops_yeojohnson
,
mlr_pipeops
Examples
library("mlr3")
task = tsk("iris")
pop = po("ica")
task$data()
#> Species Petal.Length Petal.Width Sepal.Length Sepal.Width
#> 1: setosa 1.4 0.2 5.1 3.5
#> 2: setosa 1.4 0.2 4.9 3.0
#> 3: setosa 1.3 0.2 4.7 3.2
#> 4: setosa 1.5 0.2 4.6 3.1
#> 5: setosa 1.4 0.2 5.0 3.6
#> ---
#> 146: virginica 5.2 2.3 6.7 3.0
#> 147: virginica 5.0 1.9 6.3 2.5
#> 148: virginica 5.2 2.0 6.5 3.0
#> 149: virginica 5.4 2.3 6.2 3.4
#> 150: virginica 5.1 1.8 5.9 3.0
pop$train(list(task))[[1]]$data()
#> Species V1 V2 V3 V4
#> 1: setosa 0.3732489 -0.2618717 -1.3930374 0.01866750
#> 2: setosa -0.9753637 -0.3780762 -1.3292516 -0.08399169
#> 3: setosa -0.3486723 0.3620647 -1.3478757 -0.16085744
#> 4: setosa -0.3735148 0.8821423 -1.2050671 0.31458794
#> 5: setosa 0.7376685 0.1973194 -1.3733164 0.09793575
#> ---
#> 146: virginica -0.3398829 -1.2527896 0.8398010 -2.59509417
#> 147: virginica -1.3711091 -0.7135840 0.7614490 -1.04632388
#> 148: virginica 0.0851990 -0.3381735 0.8318962 -0.98244140
#> 149: virginica 1.3897318 1.2518506 1.0580160 -1.76526213
#> 150: virginica 0.5820960 1.5464967 0.8768207 0.05304621
pop$state
#> $K
#> [,1] [,2] [,3] [,4]
#> [1,] -0.4180098 0.3531217 -0.2735163 -3.118456
#> [2,] -0.1748261 0.1537381 -1.9583093 4.897992
#> [3,] -0.1763375 -1.3373258 2.0881803 2.050340
#> [4,] 0.0412425 -1.4871770 -2.1451574 -2.077869
#>
#> $W
#> [,1] [,2] [,3] [,4]
#> [1,] -0.0690095 0.1472412 -0.98656440 -0.01576143
#> [2,] -0.7947164 0.5896582 0.14338632 0.01301322
#> [3,] -0.4790633 -0.6413534 -0.07171524 0.59499674
#> [4,] -0.3662811 -0.4682831 -0.03143217 -0.80346818
#>
#> $A
#> [,1] [,2] [,3] [,4]
#> [1,] 0.09073367 0.05162814 0.21180236 0.37079975
#> [2,] -0.16011071 -0.04299497 -0.42591385 -0.05595916
#> [3,] 1.74812487 0.73699281 0.67129316 -0.20879781
#> [4,] 0.07545899 -0.17164050 0.06499896 -0.06702598
#>
#> $center
#> Petal.Length Petal.Width Sepal.Length Sepal.Width
#> 3.758000 1.199333 5.843333 3.057333
#>
#> $dt_columns
#> [1] "Petal.Length" "Petal.Width" "Sepal.Length" "Sepal.Width"
#>
#> $affected_cols
#> [1] "Petal.Length" "Petal.Width" "Sepal.Length" "Sepal.Width"
#>
#> $intasklayout
#> id type
#> 1: Petal.Length numeric
#> 2: Petal.Width numeric
#> 3: Sepal.Length numeric
#> 4: Sepal.Width numeric
#>
#> $outtasklayout
#> id type
#> 1: V1 numeric
#> 2: V2 numeric
#> 3: V3 numeric
#> 4: V4 numeric
#>
#> $outtaskshell
#> Empty data.table (0 rows and 5 cols): Species,V1,V2,V3,V4
#>